To achieve accurate identification of cotton aphids and diseases in natural complex environments, an enhanced YOLOv9 model named YOLOv9-LSBN (Large Selective Kernel Network with Bidirectional Feature ...

To overcome this, we present an advanced detection framework, YOLOv9-AKConv-AFF-GSConv (YOLOv9-AAG), which substantially improves the accuracy of distinguishing birds from drones, even in varied poses ...

Compared to the previous generation of models, YOLOv9 achieves higher detection accuracy and speed while maintaining or reducing computational requirements, making it ideal for high-performance ...

This repository hosts an implementation of YOLOv9 integrated with Quantization-Aware Training (QAT), optimized for deployment on TensorRT-supported platforms to achieve hardware-accelerated inference.

I have developed the initial version of YOLOv9-QAT using the Q/DQ method, tailored specifically for YOLOv9 models intended for execution solely on TensorRT. This implementation currently supports only ...

Discover the transformative advancements of YOLOv9, the latest iteration of the pioneering YOLO series, enhancing efficiency and accuracy in real-time object detection through groundbreaking ...